AI systems often struggle to connect perception with reasoning in dynamic real-world environments. NVIDIA’s Cosmos Reason VLM bridges this gap by combining vision, language, and world knowledge to power intelligent video and multimodal understanding. Join this session to learn how to post-train Cosmos Reason Vision Language Model with your own data and build Vision AI agents using NVIDIA NIM microservices  and the VSS blueprint. The session will feature real-world use cases and practical guidance on creating intelligent workflows for applications across manufacturing, logistics, safety, and more.

Granite Vision is IBM’s open‑source vision‑language model, engineered to meet the demands of enterprise‑scale workflows. Our latest advancements significantly enhance the model’s ability to understand complex visual structures – such as tables, charts, and forms – and to perform high‑fidelity semantic and structured information extraction from real‑world business documents. In this session, we will highlight the technologies behind these capabilities, share key insights from our research, and demonstrate how they enable more intelligent RAG pipelines and agentic workflows, ultimately accelerating critical enterprise processes.

This talk will focus on deep-learning models for geometric alignment of time series, videos, or images, based on our recent works [Shapira Weber, ICML 2023], [Barel, ECCV 2024], [Shapira Weber, ICML 2025], [Naaman, ICCV 2025], and [Hirsch, NeurIPS 2025]. I will argue for geometry-aware choices and, when possible, regularization-free formulations, which remove the need for dataset-specific hyperparameter tuning and simplify optimization. For example, in joint alignment of images, tasks that required over an hour and per-dataset tuning in prior work can be solved in less than 50 seconds while achieving comparable or better results. 
 

Natural images are often viewed as concentrating near low-dimensional structures embedded in high-dimensional spaces. Yet in many modern approaches this geometry remains implicit, absorbed into large probabilistic models. In this talk, I revisit Blind Image Denoising from a geometric-probabilistic perspective. We couple an encoder-decoder representation with a learned distance function, interpreting restoration as iterative projection toward the set of clean and meaningful images. This viewpoint clarifies diffusion-type dynamics and leads naturally to a deterministic alternative, the Manifold-Probabilistic Projection Model (MPPM), applicable in both pixel and latent spaces and exhibiting robust behavior across diverse degradations.  

Flow models can generate images and videos based on textual descriptions. However, in many cases, it is desirable to edit real images/videos rather than generating synthetic ones. Repurposing a pre-trained flow model for performing editing has attracted significant attention in recent years. Yet, the solutions tended to struggle either with maintaining similarity to the source image/video or with adhering to the text describing the desired edit. This talk will show that the root cause for those limitations is the reliance on an “editing-by-inversion” paradigm, where the source image/video is first mapped to the initial noise that generates it. It will be demonstrated how breaking away from this paradigm allows achieving significantly better results. The proposed inversion-free, training free, and model-agnostic approaches have seen widespread adoption and integration into popular models, achieving results that compete even with training-based methods.

Autonomous driving sits at the intersection of two worlds: the geometric precision demanded by safety-critical robotics, and the open-ended semantic diversity captured by modern foundation models. To reach human-level reliability, self-driving systems must master both.

This keynote introduces a new architectural perspective that combines explicit online sensing state, semantic reasoning, and large-scale closed-loop training in artificially simulated “driving societies.” I will show how foundation models can be used not inside the high-frequency control loop, but around it: as automatic labelers, long-tail generators, anomaly detectors, and slow-thinking semantic supervisors.

By merging real fleet data with semantic simulation, by learning intentions and interactions rather than only trajectories, and by enforcing principled safety interfaces on top of learned policies, we obtain a system that scales like modern AI while satisfying the constraints of safety-critical engineering.

This architecture highlights a roadmap for the next generation of SDS: structured perception is not a limitation for end-to-end learning, but a force multiplier that enables data efficiency, reasoning, and safety in the long tail of autonomous driving.

Combining data from multiple sources often leads to better insights, yet many existing multi-view clustering methods are tailored to specific domains or require complex, multi-stage pipelines. We present a practical end-to-end deep learning framework that works across diverse data types, including images and tabular data. Our approach learns unified representations that capture shared structure across sources and enables consistent grouping without manual labels. The method is scalable, robust to noise, and supported by both theoretical insights and extensive experiments on ten benchmark datasets, demonstrating strong and reliable performance across varied real-world settings.

The integration of generative foundation models into geoscientific workflows represents a transformative shift in solving complex inverse problems. We explore advanced architectures for map synthesis via Conditional Flow Matching and volumetric inversion via 3D VAEs, leveraging magnetic, gravity, and drilling data. By constraining multi-modal generative priors with physical laws, we synthesize high-fidelity geologic insights from sparse, unorganized measurements. This approach accelerates mineral exploration by significantly reducing the cost and uncertainty of targeting subsurface anomalies. This synergy of cross-modal generative processes and potential field theory defines a new era of geologic intelligence.

ICE (intracardiac echocardiography) is a valuable tool in cardiac catheterization and electrophysiology (EP) procedures, assisting physicians in visualizing anatomical details and in monitoring procedures like catheter ablation, septal defect closure, left atrial appendage occlusion, and valve implantation.  Our aim is to automatically create an accurate three-dimensional surface model of Left atrium from automatic segmented boundaries of ICE images.  We propose a modified Poisson reconstruction method with additional geometric constraints, which enables the creation of accurate, highly detailed and computationally efficient surfaces from diverse sets of unorganized and sparse contours.

We present a technique and benchmark dataset for estimating the relative 3D orientation between a pair of Internet images captured in an extreme setting, where the images have limited or non-overlapping field of views. Prior work targeting extreme rotation estimation assume constrained 3D environments and emulate perspective images by cropping regions from panoramic views. However, real images captured in the wild are highly diverse, exhibiting variation in both appearance and camera intrinsics. In this work, we propose a Transformer-based method for estimating relative rotations in extreme real-world settings, and contribute the ExtremeLandmarkPairs dataset, assembled from scene-level Internet photo collections. Our evaluation demonstrates that our approach succeeds in estimating the relative rotations in a wide variety of extreme-view Internet image pairs, outperforming various baselines, including dedicated rotation estimation techniques and contemporary 3D reconstruction methods.

Robotic systems in real-world environments face conditions unseen during development, and while foundation models promise better generalization, integrating them under real-time onboard constraints remains challenging. We introduce BINA , a deployment-driven framework for online perceptual adaptation. BINA leverages online sparse supervision from a VLM to incrementally distil semantic knowledge into an onboard perception module. Beyond single-robot learning, BINA supports fleet-level knowledge aggregation, enabling scalable adaptation to new environments. Demonstrated on off-road traversability estimation, BINA rapidly converges from zero prior knowledge through operator-guided driving. Although demonstrated on traversability, BINA is task-agnostic and applicable to other perception and autonomy tasks. 

Controlling video generation is commonly achieved through training-based methods such as fine-tuning or adding control modules, which require extra optimization, data, or architectural changes. We propose a training-free approach that leverages pretrained video diffusion models to control object layout and motion using coarse spatio-temporal layouts. Our method operates in two passes: first, it steers spatial placement and temporal evolution through prompt-guided cross-attention to produce a coarse visual guide; second, this guide conditions the same model to generate a high-quality, layout-consistent video. The approach enables structured, coordinated motion with strong temporal consistency, supporting complex trajectories and multi-object interactions without additional training.

Controlling video generation is commonly achieved through training-based methods such as fine-tuning or adding control modules, which require extra optimization, data, or architectural changes. We propose a training-free approach that leverages pretrained video diffusion models to control object layout and motion using coarse spatio-temporal layouts. Our method operates in two passes: first, it steers spatial placement and temporal evolution through prompt-guided cross-attention to produce a coarse visual guide; second, this guide conditions the same model to generate a high-quality, layout-consistent video. The approach enables structured, coordinated motion with strong temporal consistency, supporting complex trajectories and multi-object interactions without additional training.

Accurate surface refinement in regions with fine geometric detail remains challenging in practical 3D acquisition pipelines, where reconstructed meshes are often limited by scan resolution and noise. Although many scanning systems capture high-resolution multi-view RGB imagery, exploiting these images for metric geometry refinement is difficult due to scale ambiguity and perspective effects inherent to wide field-of-view 2D projections.

We present a geometry-refinement pipeline that converts multi-view RGB observations into a consistent surface normal field and integrates it to deform an initial mesh toward a refined surface. The central approach is to use image-predicted surface normals as the primary refinement signal, providing scale-consistent geometric constraints that are not directly available from intensity values alone. Input views are selected and scored based on geometric visibility and viewpoint diversity to ensure robust coverage and stable convergence across the surface. To mitigate projection-induced distortions, images are undistorted and re-parameterized into locally aligned patches, with corresponding rotations applied to the predicted normals.

A U-Net model trained from scratch predicts normal maps on a dedicated network surface, while deformation is applied on a separate, explicitly upsampled sampling surface designed to absorb high-frequency detail beyond the resolution of the original reconstruction; an additional simplified surface supports efficient view selection and scoring. The refined normal field is fused by solving a Poisson formulation to recover metrically consistent vertex displacements. Experimental results demonstrate improved reconstruction fidelity in high-curvature and detail-critical regions, recovering subtle structures that are commonly smoothed or missing in scan-resolution-limited meshes.

A practical 15-minute technical session exploring the core principles of agentic workflows and how to move beyond single-agent limitations into coordinated, multi-agent AI systems. Attendees will learn key orchestration patterns, when to apply each, task decomposition strategies, overhead considerations, and an overview of leading frameworks. Participants will leave with a clear blueprint for designing robust multi-agent workflows and applying the right architecture for their AI applications.

Automated sports analytics often requires separate detection, OCR, and captioning models, while standard VLMs struggle to link jersey numbers to the correct player. We present a Unified Perception Engine built on Florence-2, fine-tuned to produce hierarchical, structured outputs-scene context plus player bounding boxes, orientation, and OCR-in a single pass. We use parameter-efficient fine-tuning (frozen DaViT encoder, trainable decoder), custom token weighting, and a 21-token vocabulary expansion. Trained on 47K semi-automatically annotated frames with human verification, the model achieves 97.1% precision / 91.4% recall for player-metadata association, 89.3% jersey-number exact match, and 3.2× faster inference than a three-model ensemble.

Automated sports analytics often requires separate detection, OCR, and captioning models, while standard VLMs struggle to link jersey numbers to the correct player. We present a Unified Perception Engine built on Florence-2, fine-tuned to produce hierarchical, structured outputs-scene context plus player bounding boxes, orientation, and OCR-in a single pass. We use parameter-efficient fine-tuning (frozen DaViT encoder, trainable decoder), custom token weighting, and a 21-token vocabulary expansion. Trained on 47K semi-automatically annotated frames with human verification, the model achieves 97.1% precision / 91.4% recall for player-metadata association, 89.3% jersey-number exact match, and 3.2× faster inference than a three-model ensemble.

While 2D foundation models scale remarkably, 3D vision remains constrained by data scarcity. Current methods struggle with "long-tail" distributions, often resulting in hallucinated geometry. I will present Retrieval-Augmented 3D, a framework that decouples reasoning from memorization by actively querying external databases. I will discuss two applications: RAD, utilizing uncertainty-aware retrieval to correct monocular depth, and MV-RAG, which leverages retrieved images to guide multi-view diffusion. I will conclude by demonstrating how this framework enables open-world 3D generation and understanding in the long tail.

Developing robust AI systems requires massive datasets, a significant hurdle for emerging technologies. This talk explores how to evaluate data utility early in the R&D cycle, drawing from our recent paper, "On the Effectiveness of Sparse Linear Polarization Pixels for Face Anti-Spoofing." We not only show that sparse linear polarization is highly effective for face anti-spoofing (tenfold error reduction relative to RGB), but also demonstrate that less informative representations require exponentially more training data to reach given specifications—a trend predictable using tiny datasets. This talk provides a practical case study for comparing physical representations early in research, helping teams identify the most promising technologies before hitting the "data bottleneck."

Patients with respiratory issues in emergency rooms typically undergo chest X-rays (CXR), which are accessible and low-cost but provide limited low-resolution imaging. Higher-risk patients are referred for more detailed and expensive CT or Computed Tomography Pulmonary Angiography (CTPA) scans, which involve higher radiation. The study focuses on detecting Pulmonary Embolism (PE), usually invisible in CXR but detectable in CTPA.

By leveraging paired CXR–CTPA data, we investigate two complementary diffusion-based strategies that transfer diagnostic knowledge from the high-fidelity CTPA modality to the widely available CXR domain. In the first, a conditional diffusion model
is trained to generate 3D CTPA - like representations directly from 2D CXRs, enriching the initial imaging with high-resolution vascular cues and improving PE detection performance from 69% to 80% AUC. In addition, we introduce a latent-space
diffusion prior that performs cross-modal knowledge distillation, generating CTPA-informed classifier embeddings from CXR embeddings without explicit image synthesis, enabling state-of-the-art PE classification using CXR alone.
Together, these approaches demonstrate that diffusion models can act as powerful cross-modal bridges, either through image generation or embedding level supervision, substantially enhancing early PE diagnosis from CXRs while reducing reliance on
expensive and high radiation imaging. Although not a replacement for clinical CTPA, this framework highlights a scalable and generalizable pathway for augmenting low-cost imaging with high-level diagnostic insight.
Our contributions through these works are as follows: (1) First true CXR→CTPA diffusion pipeline with diagnostic validation; (2) A novel 1D-diffusion prior for CXR→CTPA embedding distillation; (3) State-of-the-art CXR-based PE classification; (4) Modality-agnostic framework extendable to other cross-modal imaging tasks, facilitating wider access to advanced diagnostic tools.

 

SEM images used in semiconductor manufacturing pose significant challenges for vision models because labels are scarce, geometric accuracy must be maintained at sub-pixel levels, and the domain gap from natural images is substantial. To address these limitations, we introduce Warp and Render, a dual-network framework that separates geometric structure from visual appearance and enables controlled, design-guided simulation. A deformation-prediction network aligns a reference layout to the observed image, and a rendering network generates realistic SEM-like appearance from the aligned geometry. The approach generalizes across diverse pattern types and imaging conditions, remains effective in low-data regimes, and preserves strong geometric consistency, supporting high-accuracy industrial SEM-image analysis.

In the live video analysis domain, everything must happen quickly, efficiently and accurately. While traditional object detection systems rely on predefined classes, modern applications require flexibility to describe, detect, and track any object in live video streams. This brings algorithmic and computational challenges, especially for edge devices, like handling detailed attributes (e.g., “a red vintage car”), integrating specialized trackers, and managing high camera loads efficiently.

This lecture presents an algorithm for detecting and tracking objects in live video streams using detailed textual description, image examples or both. Our approach is already successfully implemented in Microsoft’s Azure AI Video Indexer.

Modern vision-language models face a fundamental accuracy-efficiency trade-off with high-resolution inputs. This talk presents four approaches to adaptive resolution across two architectural paradigms. For contrastive encoders, WAVECLIP enables coarse-to-fine processing via wavelet tokenization with early exits, while CLIMP uses Mamba architectures for natural variable-resolution support. For decoder-based VLMs, CARES predicts minimal sufficient resolution with a lightweight preprocessor (80% compute reduction), while ZoomCall trains models to selectively fetch high-resolution crops via tool-calling and reinforcement learning. These complementary strategies—progressive refinement, learned preprocessing, and agent-based reasoning—enable dynamic accuracy-efficiency trade-offs within deployed models.

We introduce Seed-to-Seed Translation (StS), a framework optimizing unpaired image-to-image translation through two primary contributions. First, we provide an in-depth analysis of the space of inverted latents ("seeds"), denoted "seed-space", demonstrating that it encodes critical semantic features for discriminative tasks. Second, we leverage these features through a novel hybrid mechanism that combines a GAN with a diffusion-model to perform unpaired seed-to-seed translation ( = image translation in the seed space) before the diffusion sampling steps start. 

We show that our method outperforms existing GAN and diffusion-based baselines in complex automotive scene synthesis,  while establishing a novel paradigm for latent-based image manipulation.

Plant temperature serves as a critical indicator of crop health, especially for identifying water stress that triggers stomatal closure and canopy heating. Although radiometric thermal IR cameras can detect such stress at an early stage, their high cost (>$20,000) restricts their use in agriculture. More affordable uncooled thermal cameras (~$4,000) present a promising alternative but suffer from drift, non-uniformity, limited accuracy (±5 °C), and low spatial resolution. To overcome these limitations, we developed deep-learning methods for non-uniformity correction (NUC) and super-resolution (SR), enhancing image resolution by factors of ×2 and ×4. In field experiments using a low-cost FLIR TAU2 alongside a scientific-grade FLIR A655Sc mounted on the same drone, our end-to-end system achieved real-time processing (<1 s per frame) with high fidelity, reducing root mean square error to ~0.5 °C. The derived Crop Water Stress Index (CWSI) closely matched reference measurements, with deviations of only ~1.4–1.9%, demonstrating that this approach enables precise, affordable, and scalable agricultural monitoring for water management.

Advancing AI in Radiology Research with NVIDIA Clara Open Medical Imaging Models - This session presents the Clara Medical Open Models, a suite of pre-trained deep learning models designed to advance research in medical image analysis. We will present the architectural principles, dataset curation strategies, and benchmarking protocols that underpin these models, emphasizing explainability, reproducibility, and domain generalization. Case studies will illustrate their application across diverse imaging modalities, including CT, MRI, and digital pathology. Through this exploration, the session will highlight how open, standardized model repositories accelerate scientific discovery and enable robust evaluation frameworks in healthcare research.

Joint Alignment (JA) aims to align a collection of images into a shared coordinate frame such that semantically corresponding features coincide spatially. Despite its importance in many vision applications, existing JA methods often rely on heavy optimization, large-capacity models, and extensive hyperparameters, leading to long training and limited scalability.

In this talk, we present FastJAM, a fast joint alignment framework that reframes JA as a graph-based problem over sparse keypoints. FastJAM leverages pairwise correspondences and a graph neural network to efficiently predict per-image transformations, achieving state-of-the-art alignment quality while reducing runtime from minutes or hours to just seconds.

Self-supervised video foundation models have recently shown strong transferability across natural video tasks, yet their applicability to domains with radically different spatiotemporal statistics remains largely unexplored. We investigate whether long-video masked autoencoders (LV-MAE), originally designed for low-frame-rate semantic natural videos, can be adapted to high-speed coherent imaging that lacks semantic structure. We apply LV-MAE to speckle-pattern video^2-3 recordings captured at 1000 fps from the scalp overlying language-related cortex during silent speech tasks. Despite extreme differences in resolution, modality, and temporal scale, LV-MAE learns transferable representations that enable accurate downstream classification with minimal labeled data. Using leave-one-subject-out evaluation with one-minute subject-specific calibration, the proposed approach achieves strong cross-subject performance on millisecond-scale inputs. These results suggest that masked video representation learning can generalize beyond natural video, enabling efficient learning in specialized high-speed imaging domains.

We present a black-box adversarial attack on diffusion models using genetic algorithms to evolve adversarial prompts. Our method injects evolved code strings to input prompts, modifying generated images to match target semantic content. Operating in a black-box setting with only image outputs, we use CLIP embeddings to measure semantic similarity. Evaluated on Stable Diffusion across multiple categories, our attack successfully manipulates image generation while maintaining perceptual quality. Multi-classifier evaluation demonstrates significant classification changes with minimal degradation, revealing vulnerabilities in diffusion model robustness.

The talk mainly covers two papers to be presented at ICML 2025. I will present our recent work analyzing the geometry of CLIP’s latent space from both geometric and probabilistic perspectives. We show that the embedding space is better characterized by two distinct, shifted ellipsoids, rather than a shared hypersphere. This finding challenges common assumptions about CLIP’s latent structure. Building on this double-ellipsoid perspective, we introduce a new measure called conformity, which captures how closely a sample aligns with its Modality Mean. Finally, I will introduce Whitened CLIP (W-CLIP) — a simple, linear transformation of the latent space into an isotropic space. It enables the use of embedding norms as a surrogate for likelihood approximation. This approach supports a wide range of applications, including domain shift detection and the identification of generative artifacts.

In Omnimatte, one aims to decompose a given video into semantically meaningful layers, including the background and individual objects along with their associated effects, such as shadows and reflections. Existing methods often require extensive training or costly self-supervised optimization. In this paper, we present OmnimatteZero, a training-free approach that leverages off-the-shelf pre-trained video diffusion models for omnimatte. It can remove objects from videos, extract individual object layers along with their effects, and composite those objects onto new videos. These are accomplished by adapting zero-shot image inpainting techniques for video object removal, a task they fail to handle effectively out-of-the-box. To overcome this, we introduce temporal and spatial attention guidance modules that steer the diffusion process for accurate object removal and temporally consistent background reconstruction. We further show that self-attention maps capture information about the object and its footprints and use them to inpaint the object's effects, leaving a clean background. Additionally, through simple latent arithmetic, object layers can be isolated and recombined seamlessly with new video layers to produce new videos. Evaluations show that OmnimatteZero not only achieves superior performance in terms of background reconstruction but also sets a new record for the fastest Omnimatte approach, achieving real-time performance with minimal frame runtime. 

Generative AI is lowering the barrier to producing plausible scientific figures, creating new risks for research integrity. We present a production-oriented approach to detecting AI-generated images in academic papers, focusing on microscopy - a domain with biological and imaging “rules,” domain-specific textures, and noise. In an internal survey domain experts found that distinguishing real vs. generated imagery is very challenging, even side-by-side. We describe a deep learning classifier trained on real literature images and synthetic images generated at scale via image-to-image and image-to-text-to-image pipelines, with publication-like compression and figure distortions. We close with lessons on the generator-detector arms race and limited explainability.

Neural models for amortized probabilistic clustering yield samples of cluster labels given a set-structured input, while avoiding lengthy Markov chain runs and the need for explicit data likelihoods. Existing methods which label each data point sequentially, like the Neural Clustering Process, often lead to cluster assignments highly dependent on the data order. Alternatively, methods that sequentially create full clusters, do not provide assignment probabilities. In this paper, we introduce GFNCP, a novel framework for  amortized clustering. GFNCP is formulated as a Generative Flow Network with a shared energy-based parametrization of policy and reward. We show that the flow matching conditions are equivalent to consistency of the clustering posterior under marginalization, which in turn implies order invariance. GFNCP also outperforms existing methods in clustering performance on both synthetic and real-world data.. The talk is based on [Chelly et. all, AISTATS '25].

Large Language Models (LLMs) and AI Agents are increasingly deployed in high-stakes human–AI systems such as video content moderation. Yet a fundamental limitation remains: they tend to respond with confidence even when they are wrong, creating significant real-world risks.

The central challenge in deploying LLMs and Agents is not maximizing autonomy, but enabling systems to recognize when an Agent should not be trusted. To address this, we introduce a trust-aware framework for human–AI collaboration in which a judge model predicts whether the LLM output should be trusted or escalated to a human.

Our approach relies on LLM Performance Predictors (LPPs) derived directly from LLM outputs, capturing confidence signals, self-reported uncertainty, and indicators of missing evidence or ambiguous decision rules. Evaluated on a large-scale multimodal moderation benchmark, our method improves performance while reducing unnecessary human intervention. These results suggest that reliable AI systems are built not by replacing humans, but by enabling models to know when to ask for human judgment.

Large Language Models (LLMs) and AI Agents are increasingly deployed in high-stakes human–AI systems such as video content moderation. Yet a fundamental limitation remains: they tend to respond with confidence even when they are wrong, creating significant real-world risks.

The central challenge in deploying LLMs and Agents is not maximizing autonomy, but enabling systems to recognize when an Agent should not be trusted. To address this, we introduce a trust-aware framework for human–AI collaboration in which a judge model predicts whether the LLM output should be trusted or escalated to a human.

Our approach relies on LLM Performance Predictors (LPPs) derived directly from LLM outputs, capturing confidence signals, self-reported uncertainty, and indicators of missing evidence or ambiguous decision rules. Evaluated on a large-scale multimodal moderation benchmark, our method improves performance while reducing unnecessary human intervention. These results suggest that reliable AI systems are built not by replacing humans, but by enabling models to know when to ask for human judgment.

This talk explores practical strategies for building generative video applications using specialized fine-tunes of LTX-2. We demonstrate how IC-LoRA enables efficient task-specific adaptations, injecting reference material and extending the capabilities of the base model. From camera control, to character consistency, we'll cover the technical considerations for using this method, training and data requirements as well as inference time challenges. This lecture is for you if you're interested in video generation, open-source video models and diverse creative use cases.

Retail shrinkage remains a major operational challenge, yet detecting cross-camera events in real stores is far from trivial. In this talk, we present a real-time multi-camera, multi-person tracking system designed specifically for sparse, pre-existing CCTV layouts, where camera placement is uncontrolled, viewpoints vary dramatically, privacy constraints imply blurred faces, and compute resources are limited.
Our approach combines calibrated visual re-identification, compact online person representations, and a learned spatiotemporal prior that models zone-to-zone transitions in an unsupervised manner, enabling robust and scalable cross-camera tracking under real-world “AI-in-the-wild” conditions.

BADAS-1.5 is Nexar’s latest V-JEPA collision prediction model, trained on large-scale real-world dashcam data. In this talk, we present the design and evolution of BADAS-1.5, focusing on how improved labeling strategies, temporal context modeling, and risk-aware training lead to a better tradeoff between recall and false positive rate.
We will share practical insights from large-scale evaluation, common failure modes, and lessons learned from deploying collision prediction models in real-world driving environments.

Enterprise documents carry critical information in their visual layout, not just their text. As RAG systems evolve to handle these multi-modal documents, new challenges emerge around evaluation, retrieval quality, and production-scale efficiency.
In this talk, I will present our team's recent work across three directions. First, building realistic benchmarks for multi-modal RAG that reflect real enterprise needs. Second, training vision-language based document retrievers that capture layout and visual semantics beyond text extraction. Third, our findings on redundancy in multi-vector document representations and how this insight enables significantly more efficient retrieval at query time.

We address the problem of looking into the water from the air, where we seek to remove image distortions caused by refractions at the water surface. Our approach is based on modeling the different water surface structures at various points in time, assuming the underlying image is constant. To this end, we propose a model that consists of two neural-field networks. The first network predicts the height of the water surface at each spatial position and time, and the second network predicts the image color at each position. Using both networks, we reconstruct the observed sequence of images and can therefore use unsupervised training. We show that using implicit neural representations with periodic activation functions (SIREN) leads to effective modeling of the surface height spatio-temporal signal and its derivative, as required for image reconstruction. Using both simulated and real data we show that our method outperforms the latest unsupervised image restoration approach. In addition, it provides an estimate of the water surface.

Convolutional Neural Networks (CNNs) have achieved outstanding success in vision and imaging domains, yet their direct application to tabular data remains limited due to the absence of spatial structure. This study introduces a novel Correlation-Based Block (Corr-Block) Transformation that converts tabular features into image-like representations, enabling CNNs to exploit inherent statistical dependencies among features. The proposed method constructs local feature blocks based on strong pairwise correlations and organizes them according to inter-block dependencies, forming near-square matrices that capture both local and global relationships. This correlation-driven layout aligns naturally with convolutional receptive fields, offering a scalable and interpretable framework for tabular deep learning. Extensive experiments on six benchmark datasets of varying sizes and dimensionalities demonstrate that the Corr-Block approach consistently outperforms both a naïve CNN baseline and the recent TablEye method, particularly in large and high-dimensional datasets where correlation structures are strong. The results highlight that embedding statistical feature relationships into spatial form allows CNNs to learn more stable and generalizable patterns. The proposed framework thus provides a promising direction for bridging tabular data modeling and image-based deep learning, paving the way for more scalable and explainable solutions in domains such as finance, healthcare, and industrial monitoring.

Machine unlearning is the task of updating a trained model to forget specific training data without retraining from scratch. This talk shows how unlearning of deep neural networks (DNNs) for image classification is affected by the model parameterization level (the DNN width). We define validation-based tuning for several unlearning methods from the recent literature, and show how these methods perform differently depending on (1) the DNN parameterization level, (2) the unlearning goal (unlearned data privacy or bias removal), (3) whether the unlearning method explicitly uses the unlearned examples. These results elucidate the practical performance and inner-workings of leading unlearning methods.

The Moore-Penrose pseudo-inverse (PInv) provides a fundamental solution for linear systems. We propose a natural generalization of PInv to the non-linear regime and to deep neural networks in particular. We introduce Surjective Pseudo-Invertible Neural Networks (SPNN), a class of architectures explicitly designed to admit a tractable non-linear PInv. The resulting construction preserves key geometric properties, including a non-linear analogue of null-space projection, formalized as Non-Linear Back-Projection (NLBP). Our framework expands the scope of zero-shot inverse problems by enabling consistent inversion of complex non-linear degradations.

Large Language Models are powerful, but they were not designed to handle visual data at massive scale. When applied directly to video and image-heavy workloads, token counts quickly explode, creating architectural bottlenecks and unsustainable costs, what we call the “50M token problem.” In this talk, Gabi will explore why visual data challenges traditional LLM pipelines and share practical strategies for building scalable, cost-efficient visual AI systems. Drawing from real-world experience processing tens of millions of videos daily, he will discuss how rethinking architecture, embeddings, and indexing is key to making visual AI viable in production.

Accurate segmentation annotations are essential for disease monitoring and large-scale medical image analysis. Yet manual labelling remains costly and time-consuming. We propose a label-efficient active learning framework for medical image segmentation that integrates a novel diversity-driven cold-start strategy with uncertainty-based sample selection. The cold start leverages foundation-model embeddings and adaptive clustering to construct a representative initial training set. During active learning, uncertainty estimation is combined with spatial diversity to guide annotation. Evaluated on X-ray and MRI datasets, the proposed framework outperforms baseline methods, particularly in low-data regimes.